As you have not provided a clue of what your models are, one can only guess. But if you mean using lots of fixed effects vs a random effect, the answer is that there is no such animal. They are two different non-nested models, and should be chosen based on subject matter considerations. Standard practice is to compare AIC, BIC and other "information criteria" but there is no clear standard to determine how large a difference is meaningful.
You may wish to post this on R-SIG-MIXED-MODELS for other inputs. Bert Sent from my iPhone -- please excuse typos. On Apr 30, 2012, at 3:21 AM, "klai...@libero.it" <klai...@libero.it> wrote: > Goodmorning everybody, > i'm an italian statistician and i'm using R for research. > > Could someone tell me some indices to see the goodness of fit in multilevel > modelling? > I'm using the lmer function, and I want to know if my model fit well my > data. > I actually want to justify the use of multilevel model instead the classical > one. > > Hope someone can help me. > Thank you. > > Greetings > Chiara > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
